def get_ticker_data(start=datetime.datetime(1940, 1, 1), end = datetime.datetime.now()):
# get all ticker price data -- we take the window of volatility
# in util.get_annualized_volatility_of_series
ret = {}
for group in TICKERS:
for ticker in TICKERS[group]:
ret[ticker] = util.get_returns(ticker, start=start, end=end)
return ret
def get_ticker_data(start=datetime.datetime(1940, 1, 1), end = datetime.datetime.now()):
# get all ticker price data -- we take the window of volatility
# in util.get_annualized_volatility_of_series
ret = {}
for group in TICKERS:
for ticker in TICKERS[group]:
ret[ticker] = util.get_returns(ticker, start=start, end=end)
return ret
def main():
start = datetime.datetime(1940, 1, 1)
end = datetime.datetime.now()
tickers = sys.argv[1:] # command line arguments
for ticker in tickers:
tick_df = util.get_returns(ticker, start, end)
tick_df['Standard Deviation (60d)'] = pd.rolling_std(tick_df['Returns'], window=60)
tick_df['Standard Deviation (200d)'] = pd.rolling_std(tick_df['Returns'], window=200)
print(ticker + " Standard Deviation")
print(np.std(tick_df['Returns']))
tick_df.to_csv("%s.csv" % ticker)
def main():
start = datetime.datetime(1940, 1, 1)
end = datetime.datetime.now()
tickers = sys.argv[1:] # command line arguments
for ticker in tickers:
tick_df = util.get_returns(ticker, start, end)
tick_df['Standard Deviation (60d)'] = pd.rolling_std(
tick_df['Returns'], window=60)
tick_df['Standard Deviation (200d)'] = pd.rolling_std(
tick_df['Returns'], window=200)
print ticker + " Standard Deviation"
print np.std(tick_df['Returns'])
tick_df.to_csv("%s.csv" % ticker)